
Hi, Rockits!
MLOps Engineer
- Machine Learning
- MLOps
- DevOps
- Kubernetes
- CI/CD
- Grafana
- Prometheus
- Bash
- Rust
- Triton Inference Server
- ONNX
- TensorRT
- GPU Optimization
- Английский — B2 — Средне-продвинутый
We are looking for an experienced MLOps Engineer to work on the project - a decentralized AI protocol on Monad that leverages idle consumer hardware for swarm inference. It enables Small Language Models to achieve advanced multi-step reasoning at lower costs, surpassing the performance and scalability of leading models.
Responsibilities:
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Deploy scalable, production-ready ML services with optimized infrastructure and auto-scaling Kubernetes clusters, create Helm templates for rapid Kubernetes node deployment.
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Optimize GPU resources using MIG (Multi-Instance GPU) and NOS (Node Offloading System);
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Manage cloud storage (e.g., S3) to ensure high availability and performance.Deploy and manage large language models (LLM), small language models (SLM), and large multimodal models (LMM);
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Serve ML models using technologies like Triton Inference Server, optimize models with ONNX and TensorRT for efficient deployment;
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Set up monitoring and logging solutions using Grafana, Prometheus, Loki, Elasticsearch, and OpenSearch;
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Write and maintain CI/CD pipelines using GitHub Actions for seamless deployment processes.
Requirements:
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5+ years of experience in MLOps or ML engineering roles;
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Proficiency in Kubernetes, Helm, and containerization technologies;
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Experience with GPU optimization (MIG, NOS) and cloud platforms (AWS, GCP, Azure);
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Strong knowledge of monitoring tools (Grafana, Prometheus) and scripting languages (Python, Bash);
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Hands-on experience with CI/CD tools and workflow management systems;
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Familiarity with Triton Inference Server, ONNX, and TensorRT for model serving and optimization.
As a plus:
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Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field;
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Experience with advanced ML techniques, such as multi-sampling and dynamic temperatures;
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Knowledge of distributed training and large model fine-tuning;
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Proficiency in Go or Rust programming languages;
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Experience designing and implementing highly secure MLOps pipelines, including secure model deployment and data encryption.